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Constraining Dark Energy with Cluster Strong Lensing

Explore how high-resolution HST observations of cluster strong lensing can constrain dark energy up to source redshifts. Understand the mass distribution’s impact on cosmic parameters using cluster arcs and multiple image geometries.

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Constraining Dark Energy with Cluster Strong Lensing

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  1. Constraining Dark Energy with Cluster Strong Lensing Priyamvada Natarajan Yale University Collaborators: Eric Jullo (JPL), Jean-Paul Kneib (OAMP), Anson D’Aloiso*(Yale), Marceau Limousin (Toulouse), Johann Richard (DARK), Carlo Schimd (OAMP)

  2. HST’s high resolution Cl2244 Post COSTAR, Hubble has provided a unique view of multiply imaged galaxies: better identification, fainter images, morphologies 1986 CFHT 1996 HST

  3. Einstein radii at multiple source redshifts Ratio of the position of multiple images,depends on mass distribution and cosmological parameters Allows constraining dark energy out to zsource!

  4. How does this work? ISOTHERMAL SPHERE LENS lens at z = zL; sources at zS1 & zS2 • EXTENDING TO MORE COMPLICATED MASS PROFILES AND MORE MULTIPLY IMAGED SOURCES………… Solve for cosmological parameters Obtained from data

  5. Cluster arcs and dark energy: Abell 1689 34 multiply imaged systems, 24 with measured redshifts Broadhurst+ 05, Benitez+ 06; Halkola+ 06; Limousin, PN+ 07; Jullo+ 2010 (Science)

  6. Strong lensingmultiple image geometries for an elliptical lens Image plane critical curves Source plane caustics

  7. Multiple image families and sensitivity to dark energy For multiple images of the same source notation denotes the position of the ith image of family f Taking the ratio of 2 distinct families of multiple images Dependence on the mass distribution Gilmore & PN 08; D’Aloisio & PN 10

  8. Mass profile of Abell 1689

  9. First results for A1689 Limousin+ 07

  10. Degeneracies….

  11. First results for A1689 Mass model with 3 PIEMD potentials; 58 cluster galaxies Bayesian optimization: 32 constraints, 21 free parameters; RMS = 0.6 arcsec; 28 multiple images from 12 sources with spec z, flat Universe prior D’Aloisio & PN 09; Jullo & Kneib 09: Jullo+ 10 (Science, August 2010, 329, 924)

  12. Requirements for cluster strong lensing • Need complement of ground based spectroscopy • Mass modeling positional accuracy • Need spectroscopic redshifts for all sources (no photo-z’s) • Structure along the line of sight behind the lens plane (environments of lenses needs to be modeled Momcheva et al. 06, Oguri, Keeton& Dalal 05) perturbations in the positions of multiple images Area under caustic likely to produce multiple images structure behind 0024

  13. Contribution of structure behind the lens plane KEY SYSTEMATICS L.O.S. SUBSTRUCTURE IN LENS PLANE & ALONG L.O.S Scaling Relations (relation between mass & light) Correlated LOS (infalling subclusters, filaments) Uncorrelated LOS(primary contribution to the errors) D’Aloisio & PN 10

  14. BIASES: choice of density profile, bimodality? Not particularly sensitive to the inner slope/outer slope of the density profile No bias from choice of profile NFW vs. PIEMD or bi-modality

  15. 10 clusters, 20 families!Flat prior, input w = -1; evolving wa Chevallier, Polarski & Linder 01

  16. Current constraints including CSL Combining X-ray clusters, WMAP5, strong lensing competitive with WMAP5 + SNe + BAO Jullo, Kneib, PN+ 10

  17. The SDSS Giant-Arc Survey & MCT Clusters…. Hennawi+ 07, 08; Oguri+ 09; Gladders+ 10; Postman+

  18. Parameter degeneracies wx For each clump: ellipticity, core radius, clump vel disp, Omegam

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